Proceedings of the 2019 9th International Conference on Communication and Network Security最新文献

Deterministic Controlled Remote State Preparation of Arbitrary Real-parameter Multi-qubit States via Three-qubit Entangled States 利用三量子位纠缠态制备任意实参数多量子位态的确定性控制远程状态

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371688

Kaihang Zhou, Lei Shi, Jiahua Wei, Yang Xue, Jie Tang

引用次数: 1

Abnormal Network Traffic Detection based on Leaf Node Density Ratio 基于叶节点密度比的网络异常流量检测

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371678

Huajun Liu, Zhanghui Liu, Yanhua Liu, Xiaoling Gao

引用次数: 2

Multi-source Landmark Fusion based on Machine Learning 基于机器学习的多源地标融合

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371694

Wen Yang, Meijuan Yin, Xiaonan Liu, Can Wang, Shunran Duan

{"title":"Multi-source Landmark Fusion based on Machine Learning","authors":"Wen Yang, Meijuan Yin, Xiaonan Liu, Can Wang, Shunran Duan","doi":"10.1145/3371676.3371694","DOIUrl":"https://doi.org/10.1145/3371676.3371694","url":null,"abstract":"Network entity landmark is the key foundation of IP geolocaiton which plays an important role in network security. Integrating multi-source landmarks to generate a landmark database with high IP coverage and high location accuracy is an important solution for improving the IP geolocation effect. However, the city-level landmarks provide the city name while street-level landmarks provide the latitude and longitude where they located. Owning to their inconsistent format, the state-of-art fusion algorithm cannot effectively integrate the two types of data. Hence, this paper proposes Lusion, a multi-source landmark fusion algorithm. We first extend the IP addresses in the landmark data sources, then model the location data of the two types of landmarks using the landmark location mixture model, and finally use the expectation -maximization algorithm to estimate the location of the landmarks. The simulation experiments on 25 landmark data sources show that the algorithm can effectively integrate the city-level and street-level landmarks from different data sources, and have a significantly better performance than the original data sources in the location accuracy. Furthermore, we evaluate Lusion on real-world datasets, which consists of 7 city-level and 3 street-level landmark data sources, by locating 100 IP addresses in Hong Kong and Zhengzhou respectively. The geolocation results show that Lusion increased the city-level accuracy by at least 8 percentage points compared with the original data sources, and reduces the geolocation error from 3.42 km to 2.92 km based on the best original landmark data set.","PeriodicalId":352443,"journal":{"name":"Proceedings of the 2019 9th International Conference on Communication and Network Security","volume":"127 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131128492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Combined Prediction Method of Industrial Internet Security Situation Based on Time Series 基于时间序列的工业互联网安全态势组合预测方法

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371704

Yingying Qi, W. Shang, Xiaojun He

{"title":"A Combined Prediction Method of Industrial Internet Security Situation Based on Time Series","authors":"Yingying Qi, W. Shang, Xiaojun He","doi":"10.1145/3371676.3371704","DOIUrl":"https://doi.org/10.1145/3371676.3371704","url":null,"abstract":"Attacks on industrial control systems have different types and various intensities. Predicting the development trend of security state for updating defense strategy in time is important. Every single prediction model has its different emphases, and the accuracy of single prediction model may be reduced when the system is attacked. Combined prediction model integrates the prediction results of multiple single prediction models to improve the overall prediction accuracy. The weight of the combined model is determined by the mean square error of every single model. It is taken a logarithmic function of the mean square error of each single model to enlarge the difference between the superior and inferior models. Then, the simple weight function is taken to determine the weight of each model based on the logarithm result of each model.This approach makes greater use of accurate model information. Through the comparative analysis of the model, the error of the combined prediction method is obviously reduced. Through the comparison and analysis of the weighted method, the error of the weighted method of the combined prediction model proposed in this paper is the minimum. The combined prediction method can provide more accurate defense opinions for network security administrators.","PeriodicalId":352443,"journal":{"name":"Proceedings of the 2019 9th International Conference on Communication and Network Security","volume":"1994 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131263668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effective Scrambling Scheme for Physical Layer Security 一种有效的物理层安全置乱方案

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371689

Enjian Bai, F. Qin, Sijing Chen

引用次数: 0

Location Privacy Protection Scheme Based on Location Services 基于位置服务的位置隐私保护方案

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371695

Chen Yang, Wei Yan

引用次数: 2

A Note on Polynomial-time Solvability for Edge Packing on Graphs 图上边填充的多项式时间可解性注记

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371691

Jiaxin Liu, Xuerong Yue, Zhibin Chen

引用次数: 0

Research on Improvement of Wireless Sensor Network Location Algorithm Based on DV-Hop 基于DV-Hop的无线传感器网络定位算法改进研究

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371698

Q. He, Cong Dai, Rong Si, Jianwen Lin

引用次数: 2

Deep Security Analysis of DCS for Ship's Nuclear Power System based on Improved DEMATEL 基于改进DEMATEL的船舶核动力系统DCS深度安全性分析

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371677

Yu Kun

引用次数: 0

A Method of Detecting the Abnormal Encrypted Traffic Based on Machine Learning and Behavior Characteristics 基于机器学习和行为特征的异常加密流量检测方法

Proceedings of the 2019 9th International Conference on Communication and Network Security Pub Date : 2019-11-15 DOI: 10.1145/3371676.3371705

Bin Kong, Zhangpu Liu, Guangmin Zhou, Xiaoyan Yu

{"title":"A Method of Detecting the Abnormal Encrypted Traffic Based on Machine Learning and Behavior Characteristics","authors":"Bin Kong, Zhangpu Liu, Guangmin Zhou, Xiaoyan Yu","doi":"10.1145/3371676.3371705","DOIUrl":"https://doi.org/10.1145/3371676.3371705","url":null,"abstract":"Classification of network traffic using port-based or deep packet-based analysis is becoming increasingly difficult with many peer-to-peer(P2P) applications using dynamic port numbers, especially in massive data streams. In view of the problem that traditional method cannot be self-learning and self-evolving in dynamic networks, this paper proposed an abnormally encrypted traffic detection method based on machine learning and behavior characteristics, this approach can not only identify unknown abnormal traffic, but eliminate specific feature extraction in advance, which can effectively improve the accuracy of the abnormal encrypted traffic detection system. In this paper, we processed the network traffic data with using a machine learning approach combined behavior characteristics of applications, the experimental results show that in the complex network, the abnormal encrypted data stream detection method based on machine learning and behavior characteristics has higher recognition accuracy and can more effectively solve the problem of abnormally encrypted traffic identification.","PeriodicalId":352443,"journal":{"name":"Proceedings of the 2019 9th International Conference on Communication and Network Security","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131416993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3